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<div class="title">imageNet.h</div>  </div>
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<a href="imageNet_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) 2017, NVIDIA CORPORATION. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> * copy of this software and associated documentation files (the &quot;Software&quot;),</span></div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> * to deal in the Software without restriction, including without limitation</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> * the rights to use, copy, modify, merge, publish, distribute, sublicense,</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> * and/or sell copies of the Software, and to permit persons to whom the</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> * Software is furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> * all copies or substantial portions of the Software.</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment"> * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> * DEALINGS IN THE SOFTWARE.</span></div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160; </div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="preprocessor">#ifndef __IMAGE_NET_H__</span></div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="preprocessor">#define __IMAGE_NET_H__</span></div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tensorNet_8h.html">tensorNet.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;</div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;</div><div class="line"><a name="l00034"></a><span class="lineno"><a class="line" href="group__imageNet.html#ga00bb3120ef3040793ad3ee25d2727f5b">   34</a></span>&#160;<span class="preprocessor">#define IMAGENET_DEFAULT_INPUT   &quot;data&quot;</span></div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;</div><div class="line"><a name="l00040"></a><span class="lineno"><a class="line" href="group__imageNet.html#ga74a585b96a1bd960b5201f6b69752fad">   40</a></span>&#160;<span class="preprocessor">#define IMAGENET_DEFAULT_OUTPUT  &quot;prob&quot;</span></div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;</div><div class="line"><a name="l00047"></a><span class="lineno"><a class="line" href="group__imageNet.html#gab0d359b9760ffe34b09adbb31d8fed54">   47</a></span>&#160;<span class="preprocessor">#define IMAGENET_USAGE_STRING  &quot;imageNet arguments: \n&quot;                                                         \</span></div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="preprocessor">                  &quot;  --network NETWORK    pre-trained model to load, one of the following:\n&quot;   \</span></div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;<span class="preprocessor">                  &quot;                           * alexnet\n&quot;                                                              \</span></div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<span class="preprocessor">                  &quot;                           * googlenet (default)\n&quot;                                  \</span></div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;<span class="preprocessor">                  &quot;                           * googlenet-12\n&quot;                                                         \</span></div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;<span class="preprocessor">                  &quot;                           * resnet-18\n&quot;                                                    \</span></div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;<span class="preprocessor">                  &quot;                           * resnet-50\n&quot;                                                    \</span></div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;<span class="preprocessor">                  &quot;                           * resnet-101\n&quot;                                                   \</span></div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;<span class="preprocessor">                  &quot;                           * resnet-152\n&quot;                                                   \</span></div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;<span class="preprocessor">                  &quot;                           * vgg-16\n&quot;                                                               \</span></div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;<span class="preprocessor">                  &quot;                           * vgg-19\n&quot;                                                               \</span></div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;<span class="preprocessor">                  &quot;                           * inception-v4\n&quot;                                                         \</span></div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;<span class="preprocessor">                  &quot;  --model MODEL        path to custom model to load (caffemodel, uff, or onnx)\n&quot;                    \</span></div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;<span class="preprocessor">                  &quot;  --prototxt PROTOTXT  path to custom prototxt to load (for .caffemodel only)\n&quot;                             \</span></div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;<span class="preprocessor">                  &quot;  --labels LABELS      path to text file containing the labels for each class\n&quot;                             \</span></div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;<span class="preprocessor">                  &quot;  --input_blob INPUT   name of the input layer (default is &#39;&quot; IMAGENET_DEFAULT_INPUT &quot;&#39;)\n&quot;  \</span></div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;<span class="preprocessor">                  &quot;  --output_blob OUTPUT name of the output layer (default is &#39;&quot; IMAGENET_DEFAULT_OUTPUT &quot;&#39;)\n&quot;        \</span></div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;<span class="preprocessor">                  &quot;  --batch_size BATCH   maximum batch size (default is 1)\n&quot;                                                          \</span></div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;<span class="preprocessor">                  &quot;  --profile            enable layer profiling in TensorRT\n&quot;</span></div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;</div><div class="line"><a name="l00072"></a><span class="lineno"><a class="line" href="classimageNet.html">   72</a></span>&#160;<span class="keyword">class </span><a class="code" href="classimageNet.html">imageNet</a> : <span class="keyword">public</span> <a class="code" href="classtensorNet.html">tensorNet</a></div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;{</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00078"></a><span class="lineno"><a class="line" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470">   78</a></span>&#160;        <span class="keyword">enum</span> <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470">NetworkType</a></div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;        {</div><div class="line"><a name="l00080"></a><span class="lineno"><a class="line" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a9237dcd407a6df5b51f027b47053b013">   80</a></span>&#160;                <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a9237dcd407a6df5b51f027b47053b013">CUSTOM</a>,        </div><div class="line"><a name="l00081"></a><span class="lineno"><a class="line" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a3e774168f30b16946773a737a6c354cf">   81</a></span>&#160;                <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a3e774168f30b16946773a737a6c354cf">ALEXNET</a>,                </div><div class="line"><a name="l00082"></a><span class="lineno"><a class="line" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470ab013750d9b65eacdae3c587dd42550c0">   82</a></span>&#160;                <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470ab013750d9b65eacdae3c587dd42550c0">GOOGLENET</a>,      </div><div class="line"><a name="l00083"></a><span class="lineno"><a class="line" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470af4a7d4831db43dda4de80c2a395f3ebb">   83</a></span>&#160;                <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470af4a7d4831db43dda4de80c2a395f3ebb">GOOGLENET_12</a>,   </div><div class="line"><a name="l00084"></a><span class="lineno"><a class="line" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a41ccbd9480e0072ed579da95eb6a479d">   84</a></span>&#160;                <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a41ccbd9480e0072ed579da95eb6a479d">RESNET_18</a>,      </div><div class="line"><a name="l00085"></a><span class="lineno"><a class="line" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470aa940343d369f0026258f0a42188a405b">   85</a></span>&#160;                <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470aa940343d369f0026258f0a42188a405b">RESNET_50</a>,      </div><div class="line"><a name="l00086"></a><span class="lineno"><a class="line" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a3bc3f084f3ef071585caf9629e64e24b">   86</a></span>&#160;                <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a3bc3f084f3ef071585caf9629e64e24b">RESNET_101</a>,     </div><div class="line"><a name="l00087"></a><span class="lineno"><a class="line" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a91e56faa59f440c7aeb42f37363f27c4">   87</a></span>&#160;                <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a91e56faa59f440c7aeb42f37363f27c4">RESNET_152</a>,     </div><div class="line"><a name="l00088"></a><span class="lineno"><a class="line" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a4a1ff14a90c30b505d6a2e563ad02bdc">   88</a></span>&#160;                <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a4a1ff14a90c30b505d6a2e563ad02bdc">VGG_16</a>,         </div><div class="line"><a name="l00089"></a><span class="lineno"><a class="line" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470ab44362ca647faaeb0b4a3124aef6a6fa">   89</a></span>&#160;                <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470ab44362ca647faaeb0b4a3124aef6a6fa">VGG_19</a>,         </div><div class="line"><a name="l00090"></a><span class="lineno"><a class="line" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a196d67c5fe33ca0e6724b0dfdff0a8e0">   90</a></span>&#160;                <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a196d67c5fe33ca0e6724b0dfdff0a8e0">INCEPTION_V4</a>,   </div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        };</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;        <span class="keyword">static</span> <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470">NetworkType</a> <a class="code" href="classimageNet.html#a911888acec5ff79f63e42ecdaed4d9c5">NetworkTypeFromStr</a>( <span class="keyword">const</span> <span class="keywordtype">char</span>* model_name );</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;        <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="classimageNet.html#a0a6ed78a812c2c8847aec7e2c9c7ecab">NetworkTypeToStr</a>( <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470">NetworkType</a> network );</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;        <span class="keyword">static</span> <a class="code" href="classimageNet.html">imageNet</a>* <a class="code" href="classimageNet.html#a27823449de3babc8b6eff1e916aff745">Create</a>( <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470">NetworkType</a> networkType=<a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470ab013750d9b65eacdae3c587dd42550c0">GOOGLENET</a>, uint32_t maxBatchSize=<a class="code" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, </div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;                                                <a class="code" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="code" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>,</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;                                                <a class="code" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="code" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, <span class="keywordtype">bool</span> allowGPUFallback=<span class="keyword">true</span> );</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;        </div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;        <span class="keyword">static</span> <a class="code" href="classimageNet.html">imageNet</a>* <a class="code" href="classimageNet.html#a27823449de3babc8b6eff1e916aff745">Create</a>( <span class="keyword">const</span> <span class="keywordtype">char</span>* prototxt_path, <span class="keyword">const</span> <span class="keywordtype">char</span>* model_path, </div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;                                                <span class="keyword">const</span> <span class="keywordtype">char</span>* mean_binary, <span class="keyword">const</span> <span class="keywordtype">char</span>* class_labels, </div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;                                                <span class="keyword">const</span> <span class="keywordtype">char</span>* input=<a class="code" href="group__imageNet.html#ga00bb3120ef3040793ad3ee25d2727f5b">IMAGENET_DEFAULT_INPUT</a>, </div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;                                                <span class="keyword">const</span> <span class="keywordtype">char</span>* output=<a class="code" href="group__imageNet.html#ga74a585b96a1bd960b5201f6b69752fad">IMAGENET_DEFAULT_OUTPUT</a>, </div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;                                                uint32_t maxBatchSize=<a class="code" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, </div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;                                                <a class="code" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="code" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>,</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;                                                <a class="code" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="code" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, <span class="keywordtype">bool</span> allowGPUFallback=<span class="keyword">true</span> );</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;        </div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        <span class="keyword">static</span> <a class="code" href="classimageNet.html">imageNet</a>* <a class="code" href="classimageNet.html#a27823449de3babc8b6eff1e916aff745">Create</a>( <span class="keywordtype">int</span> argc, <span class="keywordtype">char</span>** argv );</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;</div><div class="line"><a name="l00138"></a><span class="lineno"><a class="line" href="classimageNet.html#a7629a888728ef94bf35e573a96ebe4bd">  138</a></span>&#160;        <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="classimageNet.html#a7629a888728ef94bf35e573a96ebe4bd">Usage</a>()               { <span class="keywordflow">return</span> <a class="code" href="group__imageNet.html#gab0d359b9760ffe34b09adbb31d8fed54">IMAGENET_USAGE_STRING</a>; }</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;        <span class="keyword">virtual</span> <a class="code" href="classimageNet.html#af6bd86e81ff9e67ffe19b575c17ed104">~imageNet</a>();</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;        </div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;        <span class="keywordtype">int</span> <a class="code" href="classimageNet.html#a11d6b18e0036ef5a329192f8b659dff9">Classify</a>( <span class="keywordtype">float</span>* rgba, uint32_t width, uint32_t height, <span class="keywordtype">float</span>* confidence=NULL );</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;        <span class="keywordtype">int</span> <a class="code" href="classimageNet.html#a11d6b18e0036ef5a329192f8b659dff9">Classify</a>( <span class="keywordtype">float</span>* confidence=NULL );</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;        <span class="keywordtype">bool</span> <a class="code" href="classimageNet.html#a83c68fbe0faab1e88abf07f7b535b8b9">PreProcess</a>( <span class="keywordtype">float</span>* rgba, uint32_t width, uint32_t height );</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;        <span class="keywordtype">bool</span> <a class="code" href="classimageNet.html#ad9eb86e82a3a2a05700e3c36f9554e64">Process</a>();</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;</div><div class="line"><a name="l00180"></a><span class="lineno"><a class="line" href="classimageNet.html#a478f25126524a256e81ec264aad7e27a">  180</a></span>&#160;        <span class="keyword">inline</span> uint32_t <a class="code" href="classimageNet.html#a478f25126524a256e81ec264aad7e27a">GetNumClasses</a>()<span class="keyword"> const                                           </span>{ <span class="keywordflow">return</span> <a class="code" href="classimageNet.html#a80749925c9b6edf6b49043e8a0f507e3">mOutputClasses</a>; }</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        </div><div class="line"><a name="l00185"></a><span class="lineno"><a class="line" href="classimageNet.html#a42ce2aeeb96379bd04d94f65d483ece1">  185</a></span>&#160;        <span class="keyword">inline</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="classimageNet.html#a42ce2aeeb96379bd04d94f65d483ece1">GetClassDesc</a>( uint32_t index )<span class="keyword">       const           </span>{ <span class="keywordflow">return</span> <a class="code" href="classimageNet.html#a9c75cea83d0c3e605aef8c0dd8e43177">mClassDesc</a>[index].c_str(); }</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;        </div><div class="line"><a name="l00190"></a><span class="lineno"><a class="line" href="classimageNet.html#a343bbe0aad580411900f811f30e8cbf7">  190</a></span>&#160;        <span class="keyword">inline</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="classimageNet.html#a343bbe0aad580411900f811f30e8cbf7">GetClassSynset</a>( uint32_t index )<span class="keyword"> const               </span>{ <span class="keywordflow">return</span> <a class="code" href="classimageNet.html#abd00b812a1f39a0bd23c43a8807d6193">mClassSynset</a>[index].c_str(); }</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;        </div><div class="line"><a name="l00195"></a><span class="lineno"><a class="line" href="classimageNet.html#a04276f915b0f40d6257cbed3fe47dc5f">  195</a></span>&#160;        <span class="keyword">inline</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="classimageNet.html#a04276f915b0f40d6257cbed3fe47dc5f">GetClassPath</a>()<span class="keyword"> const                                         </span>{ <span class="keywordflow">return</span> <a class="code" href="classimageNet.html#a7bce88c4d67550b5d059a4b9cdbb90c1">mClassPath</a>.c_str(); }</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;</div><div class="line"><a name="l00200"></a><span class="lineno"><a class="line" href="classimageNet.html#ad0cde8f64f32a50984c947dc823de04e">  200</a></span>&#160;        <span class="keyword">inline</span> <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470">NetworkType</a> <a class="code" href="classimageNet.html#ad0cde8f64f32a50984c947dc823de04e">GetNetworkType</a>()<span class="keyword"> const                                       </span>{ <span class="keywordflow">return</span> <a class="code" href="classimageNet.html#aef7f06f334699634c33b1243c4352fc9">mNetworkType</a>; }</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;</div><div class="line"><a name="l00205"></a><span class="lineno"><a class="line" href="classimageNet.html#a359ecefb8ac20cd44fddc69d38bde3ef">  205</a></span>&#160;        <span class="keyword">inline</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="classimageNet.html#a359ecefb8ac20cd44fddc69d38bde3ef">GetNetworkName</a>()<span class="keyword"> const                                       </span>{ <a class="code" href="classimageNet.html#a0a6ed78a812c2c8847aec7e2c9c7ecab">NetworkTypeToStr</a>(<a class="code" href="classimageNet.html#aef7f06f334699634c33b1243c4352fc9">mNetworkType</a>); }</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;        <span class="keyword">static</span> <span class="keywordtype">bool</span> <a class="code" href="classimageNet.html#a874e53e6172211c555031e50c6391f98">LoadClassInfo</a>( <span class="keyword">const</span> <span class="keywordtype">char</span>* filename, std::vector&lt;std::string&gt;&amp; descriptions, <span class="keywordtype">int</span> expectedClasses=-1 );</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;        <span class="keyword">static</span> <span class="keywordtype">bool</span> <a class="code" href="classimageNet.html#a874e53e6172211c555031e50c6391f98">LoadClassInfo</a>( <span class="keyword">const</span> <span class="keywordtype">char</span>* filename, std::vector&lt;std::string&gt;&amp; descriptions, std::vector&lt;std::string&gt;&amp; synsets, <span class="keywordtype">int</span> expectedClasses=-1 );</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;<span class="keyword">protected</span>:</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;        <a class="code" href="classimageNet.html#a0ea17be1ce78b3e0758af46c970a968c">imageNet</a>();</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;        </div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;        <span class="keywordtype">bool</span> <a class="code" href="classimageNet.html#a84dd4bae637b43560c6a1ca71e1df3fe">init</a>( <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470">NetworkType</a> networkType, uint32_t maxBatchSize, <a class="code" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="code" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device, <span class="keywordtype">bool</span> allowGPUFallback );</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;        <span class="keywordtype">bool</span> <a class="code" href="classimageNet.html#a84dd4bae637b43560c6a1ca71e1df3fe">init</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* prototxt_path, <span class="keyword">const</span> <span class="keywordtype">char</span>* model_path, <span class="keyword">const</span> <span class="keywordtype">char</span>* mean_binary, <span class="keyword">const</span> <span class="keywordtype">char</span>* class_path, <span class="keyword">const</span> <span class="keywordtype">char</span>* input, <span class="keyword">const</span> <span class="keywordtype">char</span>* output, uint32_t maxBatchSize, <a class="code" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="code" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device, <span class="keywordtype">bool</span> allowGPUFallback );</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;        <span class="keywordtype">bool</span> <a class="code" href="classimageNet.html#a6beef2c8d0972eaadad37abc89e74f95">loadClassInfo</a>( <span class="keyword">const</span> <span class="keywordtype">char</span>* filename, <span class="keywordtype">int</span> expectedClasses=-1 );</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;        </div><div class="line"><a name="l00224"></a><span class="lineno"><a class="line" href="classimageNet.html#a80749925c9b6edf6b49043e8a0f507e3">  224</a></span>&#160;        uint32_t <a class="code" href="classimageNet.html#a80749925c9b6edf6b49043e8a0f507e3">mOutputClasses</a>;</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;        </div><div class="line"><a name="l00226"></a><span class="lineno"><a class="line" href="classimageNet.html#abd00b812a1f39a0bd23c43a8807d6193">  226</a></span>&#160;        std::vector&lt;std::string&gt; <a class="code" href="classimageNet.html#abd00b812a1f39a0bd23c43a8807d6193">mClassSynset</a>;  <span class="comment">// 1000 class ID&#39;s (ie n01580077, n04325704)</span></div><div class="line"><a name="l00227"></a><span class="lineno"><a class="line" href="classimageNet.html#a9c75cea83d0c3e605aef8c0dd8e43177">  227</a></span>&#160;        std::vector&lt;std::string&gt; <a class="code" href="classimageNet.html#a9c75cea83d0c3e605aef8c0dd8e43177">mClassDesc</a>;</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;</div><div class="line"><a name="l00229"></a><span class="lineno"><a class="line" href="classimageNet.html#a7bce88c4d67550b5d059a4b9cdbb90c1">  229</a></span>&#160;        std::string <a class="code" href="classimageNet.html#a7bce88c4d67550b5d059a4b9cdbb90c1">mClassPath</a>;</div><div class="line"><a name="l00230"></a><span class="lineno"><a class="line" href="classimageNet.html#aef7f06f334699634c33b1243c4352fc9">  230</a></span>&#160;        <a class="code" href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470">NetworkType</a> <a class="code" href="classimageNet.html#aef7f06f334699634c33b1243c4352fc9">mNetworkType</a>;</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;};</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;<span class="preprocessor">#endif</span></div><div class="ttc" id="group__tensorNet_html_gaac6604fd52c6e5db82877390e0378623"><div class="ttname"><a href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a></div><div class="ttdeci">precisionType</div><div class="ttdoc">Enumeration for indicating the desired precision that the network should run in, if available in hard...</div><div class="ttdef"><b>Definition:</b> tensorNet.h:79</div></div>
<div class="ttc" id="classimageNet_html_a04276f915b0f40d6257cbed3fe47dc5f"><div class="ttname"><a href="classimageNet.html#a04276f915b0f40d6257cbed3fe47dc5f">imageNet::GetClassPath</a></div><div class="ttdeci">const char * GetClassPath() const</div><div class="ttdoc">Retrieve the path to the file containing the class descriptions. </div><div class="ttdef"><b>Definition:</b> imageNet.h:195</div></div>
<div class="ttc" id="group__tensorNet_html_ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b"><div class="ttname"><a href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a></div><div class="ttdoc">GPU (if multiple GPUs are present, a specific GPU can be selected with cudaSetDevice() ...</div><div class="ttdef"><b>Definition:</b> tensorNet.h:108</div></div>
<div class="ttc" id="classimageNet_html_a343bbe0aad580411900f811f30e8cbf7"><div class="ttname"><a href="classimageNet.html#a343bbe0aad580411900f811f30e8cbf7">imageNet::GetClassSynset</a></div><div class="ttdeci">const char * GetClassSynset(uint32_t index) const</div><div class="ttdoc">Retrieve the class synset category of a particular class. </div><div class="ttdef"><b>Definition:</b> imageNet.h:190</div></div>
<div class="ttc" id="group__imageNet_html_ga00bb3120ef3040793ad3ee25d2727f5b"><div class="ttname"><a href="group__imageNet.html#ga00bb3120ef3040793ad3ee25d2727f5b">IMAGENET_DEFAULT_INPUT</a></div><div class="ttdeci">#define IMAGENET_DEFAULT_INPUT</div><div class="ttdoc">Name of default input blob for imageNet model. </div><div class="ttdef"><b>Definition:</b> imageNet.h:34</div></div>
<div class="ttc" id="classimageNet_html_a84dd4bae637b43560c6a1ca71e1df3fe"><div class="ttname"><a href="classimageNet.html#a84dd4bae637b43560c6a1ca71e1df3fe">imageNet::init</a></div><div class="ttdeci">bool init(NetworkType networkType, uint32_t maxBatchSize, precisionType precision, deviceType device, bool allowGPUFallback)</div></div>
<div class="ttc" id="classimageNet_html_a0b7e93af566fe96bfc58cda5f4503470ab013750d9b65eacdae3c587dd42550c0"><div class="ttname"><a href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470ab013750d9b65eacdae3c587dd42550c0">imageNet::GOOGLENET</a></div><div class="ttdoc">GoogleNet trained 1000-class ILSVRC12. </div><div class="ttdef"><b>Definition:</b> imageNet.h:82</div></div>
<div class="ttc" id="classimageNet_html_a478f25126524a256e81ec264aad7e27a"><div class="ttname"><a href="classimageNet.html#a478f25126524a256e81ec264aad7e27a">imageNet::GetNumClasses</a></div><div class="ttdeci">uint32_t GetNumClasses() const</div><div class="ttdoc">Retrieve the number of image recognition classes (typically 1000) </div><div class="ttdef"><b>Definition:</b> imageNet.h:180</div></div>
<div class="ttc" id="classimageNet_html_a42ce2aeeb96379bd04d94f65d483ece1"><div class="ttname"><a href="classimageNet.html#a42ce2aeeb96379bd04d94f65d483ece1">imageNet::GetClassDesc</a></div><div class="ttdeci">const char * GetClassDesc(uint32_t index) const</div><div class="ttdoc">Retrieve the description of a particular class. </div><div class="ttdef"><b>Definition:</b> imageNet.h:185</div></div>
<div class="ttc" id="group__tensorNet_html_gaa5d3f9981cdbd91516c1474006a80fe4"><div class="ttname"><a href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a></div><div class="ttdeci">deviceType</div><div class="ttdoc">Enumeration for indicating the desired device that the network should run on, if available in hardwar...</div><div class="ttdef"><b>Definition:</b> tensorNet.h:106</div></div>
<div class="ttc" id="group__imageNet_html_ga74a585b96a1bd960b5201f6b69752fad"><div class="ttname"><a href="group__imageNet.html#ga74a585b96a1bd960b5201f6b69752fad">IMAGENET_DEFAULT_OUTPUT</a></div><div class="ttdeci">#define IMAGENET_DEFAULT_OUTPUT</div><div class="ttdoc">Name of default output confidence values for imageNet model. </div><div class="ttdef"><b>Definition:</b> imageNet.h:40</div></div>
<div class="ttc" id="classimageNet_html_a874e53e6172211c555031e50c6391f98"><div class="ttname"><a href="classimageNet.html#a874e53e6172211c555031e50c6391f98">imageNet::LoadClassInfo</a></div><div class="ttdeci">static bool LoadClassInfo(const char *filename, std::vector&lt; std::string &gt; &amp;descriptions, int expectedClasses=-1)</div><div class="ttdoc">Load class descriptions from a label file. </div></div>
<div class="ttc" id="group__tensorNet_html_ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9"><div class="ttname"><a href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a></div><div class="ttdoc">The fastest detected precision should be use (i.e. </div><div class="ttdef"><b>Definition:</b> tensorNet.h:82</div></div>
<div class="ttc" id="classimageNet_html_a0a6ed78a812c2c8847aec7e2c9c7ecab"><div class="ttname"><a href="classimageNet.html#a0a6ed78a812c2c8847aec7e2c9c7ecab">imageNet::NetworkTypeToStr</a></div><div class="ttdeci">static const char * NetworkTypeToStr(NetworkType network)</div><div class="ttdoc">Convert a NetworkType enum to a string. </div></div>
<div class="ttc" id="tensorNet_8h_html"><div class="ttname"><a href="tensorNet_8h.html">tensorNet.h</a></div></div>
<div class="ttc" id="classimageNet_html_a6beef2c8d0972eaadad37abc89e74f95"><div class="ttname"><a href="classimageNet.html#a6beef2c8d0972eaadad37abc89e74f95">imageNet::loadClassInfo</a></div><div class="ttdeci">bool loadClassInfo(const char *filename, int expectedClasses=-1)</div></div>
<div class="ttc" id="classimageNet_html_a0b7e93af566fe96bfc58cda5f4503470a9237dcd407a6df5b51f027b47053b013"><div class="ttname"><a href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a9237dcd407a6df5b51f027b47053b013">imageNet::CUSTOM</a></div><div class="ttdoc">Custom model provided by the user. </div><div class="ttdef"><b>Definition:</b> imageNet.h:80</div></div>
<div class="ttc" id="classimageNet_html_a359ecefb8ac20cd44fddc69d38bde3ef"><div class="ttname"><a href="classimageNet.html#a359ecefb8ac20cd44fddc69d38bde3ef">imageNet::GetNetworkName</a></div><div class="ttdeci">const char * GetNetworkName() const</div><div class="ttdoc">Retrieve a string describing the network name. </div><div class="ttdef"><b>Definition:</b> imageNet.h:205</div></div>
<div class="ttc" id="classimageNet_html_a0b7e93af566fe96bfc58cda5f4503470a4a1ff14a90c30b505d6a2e563ad02bdc"><div class="ttname"><a href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a4a1ff14a90c30b505d6a2e563ad02bdc">imageNet::VGG_16</a></div><div class="ttdoc">VGG-16 trained on 1000-class ILSVRC14. </div><div class="ttdef"><b>Definition:</b> imageNet.h:88</div></div>
<div class="ttc" id="classimageNet_html_a83c68fbe0faab1e88abf07f7b535b8b9"><div class="ttname"><a href="classimageNet.html#a83c68fbe0faab1e88abf07f7b535b8b9">imageNet::PreProcess</a></div><div class="ttdeci">bool PreProcess(float *rgba, uint32_t width, uint32_t height)</div><div class="ttdoc">Perform pre-processing on the image to apply mean-value subtraction and to organize the data into NCH...</div></div>
<div class="ttc" id="classimageNet_html_a0b7e93af566fe96bfc58cda5f4503470aa940343d369f0026258f0a42188a405b"><div class="ttname"><a href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470aa940343d369f0026258f0a42188a405b">imageNet::RESNET_50</a></div><div class="ttdoc">ResNet-50 trained on 1000-class ILSVRC15. </div><div class="ttdef"><b>Definition:</b> imageNet.h:85</div></div>
<div class="ttc" id="classimageNet_html_ad9eb86e82a3a2a05700e3c36f9554e64"><div class="ttname"><a href="classimageNet.html#ad9eb86e82a3a2a05700e3c36f9554e64">imageNet::Process</a></div><div class="ttdeci">bool Process()</div><div class="ttdoc">Process the network, without determining the classification argmax. </div></div>
<div class="ttc" id="classimageNet_html_a0b7e93af566fe96bfc58cda5f4503470"><div class="ttname"><a href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470">imageNet::NetworkType</a></div><div class="ttdeci">NetworkType</div><div class="ttdoc">Network choice enumeration. </div><div class="ttdef"><b>Definition:</b> imageNet.h:78</div></div>
<div class="ttc" id="classimageNet_html_aef7f06f334699634c33b1243c4352fc9"><div class="ttname"><a href="classimageNet.html#aef7f06f334699634c33b1243c4352fc9">imageNet::mNetworkType</a></div><div class="ttdeci">NetworkType mNetworkType</div><div class="ttdef"><b>Definition:</b> imageNet.h:230</div></div>
<div class="ttc" id="classimageNet_html_a0b7e93af566fe96bfc58cda5f4503470a91e56faa59f440c7aeb42f37363f27c4"><div class="ttname"><a href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a91e56faa59f440c7aeb42f37363f27c4">imageNet::RESNET_152</a></div><div class="ttdoc">ResNet-50 trained on 1000-class ILSVRC15. </div><div class="ttdef"><b>Definition:</b> imageNet.h:87</div></div>
<div class="ttc" id="classimageNet_html"><div class="ttname"><a href="classimageNet.html">imageNet</a></div><div class="ttdoc">Image recognition with classification networks, using TensorRT. </div><div class="ttdef"><b>Definition:</b> imageNet.h:72</div></div>
<div class="ttc" id="classimageNet_html_a11d6b18e0036ef5a329192f8b659dff9"><div class="ttname"><a href="classimageNet.html#a11d6b18e0036ef5a329192f8b659dff9">imageNet::Classify</a></div><div class="ttdeci">int Classify(float *rgba, uint32_t width, uint32_t height, float *confidence=NULL)</div><div class="ttdoc">Determine the maximum likelihood image class. </div></div>
<div class="ttc" id="group__tensorNet_html_ga5a46a965749d6118e01307fd4d4865c9"><div class="ttname"><a href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a></div><div class="ttdeci">#define DEFAULT_MAX_BATCH_SIZE</div><div class="ttdoc">Default maximum batch size. </div><div class="ttdef"><b>Definition:</b> tensorNet.h:65</div></div>
<div class="ttc" id="classtensorNet_html"><div class="ttname"><a href="classtensorNet.html">tensorNet</a></div><div class="ttdoc">Abstract class for loading a tensor network with TensorRT. </div><div class="ttdef"><b>Definition:</b> tensorNet.h:188</div></div>
<div class="ttc" id="classimageNet_html_abd00b812a1f39a0bd23c43a8807d6193"><div class="ttname"><a href="classimageNet.html#abd00b812a1f39a0bd23c43a8807d6193">imageNet::mClassSynset</a></div><div class="ttdeci">std::vector&lt; std::string &gt; mClassSynset</div><div class="ttdef"><b>Definition:</b> imageNet.h:226</div></div>
<div class="ttc" id="classimageNet_html_a0b7e93af566fe96bfc58cda5f4503470a3bc3f084f3ef071585caf9629e64e24b"><div class="ttname"><a href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a3bc3f084f3ef071585caf9629e64e24b">imageNet::RESNET_101</a></div><div class="ttdoc">ResNet-101 trained on 1000-class ILSVRC15. </div><div class="ttdef"><b>Definition:</b> imageNet.h:86</div></div>
<div class="ttc" id="classimageNet_html_a0ea17be1ce78b3e0758af46c970a968c"><div class="ttname"><a href="classimageNet.html#a0ea17be1ce78b3e0758af46c970a968c">imageNet::imageNet</a></div><div class="ttdeci">imageNet()</div></div>
<div class="ttc" id="classimageNet_html_a9c75cea83d0c3e605aef8c0dd8e43177"><div class="ttname"><a href="classimageNet.html#a9c75cea83d0c3e605aef8c0dd8e43177">imageNet::mClassDesc</a></div><div class="ttdeci">std::vector&lt; std::string &gt; mClassDesc</div><div class="ttdef"><b>Definition:</b> imageNet.h:227</div></div>
<div class="ttc" id="classimageNet_html_a0b7e93af566fe96bfc58cda5f4503470a3e774168f30b16946773a737a6c354cf"><div class="ttname"><a href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a3e774168f30b16946773a737a6c354cf">imageNet::ALEXNET</a></div><div class="ttdoc">AlexNet trained on 1000-class ILSVRC12. </div><div class="ttdef"><b>Definition:</b> imageNet.h:81</div></div>
<div class="ttc" id="classimageNet_html_a0b7e93af566fe96bfc58cda5f4503470af4a7d4831db43dda4de80c2a395f3ebb"><div class="ttname"><a href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470af4a7d4831db43dda4de80c2a395f3ebb">imageNet::GOOGLENET_12</a></div><div class="ttdoc">GoogleNet trained on 12-class subset of ImageNet ILSVRC12 from the tutorial. </div><div class="ttdef"><b>Definition:</b> imageNet.h:83</div></div>
<div class="ttc" id="classimageNet_html_a7bce88c4d67550b5d059a4b9cdbb90c1"><div class="ttname"><a href="classimageNet.html#a7bce88c4d67550b5d059a4b9cdbb90c1">imageNet::mClassPath</a></div><div class="ttdeci">std::string mClassPath</div><div class="ttdef"><b>Definition:</b> imageNet.h:229</div></div>
<div class="ttc" id="classimageNet_html_a911888acec5ff79f63e42ecdaed4d9c5"><div class="ttname"><a href="classimageNet.html#a911888acec5ff79f63e42ecdaed4d9c5">imageNet::NetworkTypeFromStr</a></div><div class="ttdeci">static NetworkType NetworkTypeFromStr(const char *model_name)</div><div class="ttdoc">Parse a string to one of the built-in pretrained models. </div></div>
<div class="ttc" id="group__imageNet_html_gab0d359b9760ffe34b09adbb31d8fed54"><div class="ttname"><a href="group__imageNet.html#gab0d359b9760ffe34b09adbb31d8fed54">IMAGENET_USAGE_STRING</a></div><div class="ttdeci">#define IMAGENET_USAGE_STRING</div><div class="ttdoc">Command-line options able to be passed to imageNet::Create() </div><div class="ttdef"><b>Definition:</b> imageNet.h:47</div></div>
<div class="ttc" id="classimageNet_html_a0b7e93af566fe96bfc58cda5f4503470a41ccbd9480e0072ed579da95eb6a479d"><div class="ttname"><a href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a41ccbd9480e0072ed579da95eb6a479d">imageNet::RESNET_18</a></div><div class="ttdoc">ResNet-18 trained on 1000-class ILSVRC15. </div><div class="ttdef"><b>Definition:</b> imageNet.h:84</div></div>
<div class="ttc" id="classimageNet_html_a0b7e93af566fe96bfc58cda5f4503470a196d67c5fe33ca0e6724b0dfdff0a8e0"><div class="ttname"><a href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470a196d67c5fe33ca0e6724b0dfdff0a8e0">imageNet::INCEPTION_V4</a></div><div class="ttdoc">Inception-v4 trained on 1000-class ILSVRC12. </div><div class="ttdef"><b>Definition:</b> imageNet.h:90</div></div>
<div class="ttc" id="classimageNet_html_a80749925c9b6edf6b49043e8a0f507e3"><div class="ttname"><a href="classimageNet.html#a80749925c9b6edf6b49043e8a0f507e3">imageNet::mOutputClasses</a></div><div class="ttdeci">uint32_t mOutputClasses</div><div class="ttdef"><b>Definition:</b> imageNet.h:224</div></div>
<div class="ttc" id="classimageNet_html_ad0cde8f64f32a50984c947dc823de04e"><div class="ttname"><a href="classimageNet.html#ad0cde8f64f32a50984c947dc823de04e">imageNet::GetNetworkType</a></div><div class="ttdeci">NetworkType GetNetworkType() const</div><div class="ttdoc">Retrieve the network type (alexnet or googlenet) </div><div class="ttdef"><b>Definition:</b> imageNet.h:200</div></div>
<div class="ttc" id="classimageNet_html_af6bd86e81ff9e67ffe19b575c17ed104"><div class="ttname"><a href="classimageNet.html#af6bd86e81ff9e67ffe19b575c17ed104">imageNet::~imageNet</a></div><div class="ttdeci">virtual ~imageNet()</div><div class="ttdoc">Destroy. </div></div>
<div class="ttc" id="classimageNet_html_a0b7e93af566fe96bfc58cda5f4503470ab44362ca647faaeb0b4a3124aef6a6fa"><div class="ttname"><a href="classimageNet.html#a0b7e93af566fe96bfc58cda5f4503470ab44362ca647faaeb0b4a3124aef6a6fa">imageNet::VGG_19</a></div><div class="ttdoc">VGG-19 trained on 1000-class ILSVRC14. </div><div class="ttdef"><b>Definition:</b> imageNet.h:89</div></div>
<div class="ttc" id="classimageNet_html_a7629a888728ef94bf35e573a96ebe4bd"><div class="ttname"><a href="classimageNet.html#a7629a888728ef94bf35e573a96ebe4bd">imageNet::Usage</a></div><div class="ttdeci">static const char * Usage()</div><div class="ttdoc">Usage string for command line arguments to Create() </div><div class="ttdef"><b>Definition:</b> imageNet.h:138</div></div>
<div class="ttc" id="classimageNet_html_a27823449de3babc8b6eff1e916aff745"><div class="ttname"><a href="classimageNet.html#a27823449de3babc8b6eff1e916aff745">imageNet::Create</a></div><div class="ttdeci">static imageNet * Create(NetworkType networkType=GOOGLENET, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true)</div><div class="ttdoc">Load a new network instance. </div></div>
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